Virulence Evolution in Little Appleton
Meghan Duffy
4/2/2021
Data and code for paper on evolution during a 2017 epidemic of Pasteuria ramosa in Little Appleton Lake. Authors: Camden D. Gowler Haley Essington Patrick A. Clay Bruce O’Brien Clara L. Shaw Rebecca Bilich Meghan A. Duffy
Analysis of data collected on Little Appleton’s 2017 Pasteuria epidemic
# Calculating the proportion that never reproduced
LA_infected <- LA_infected %>%
mutate(anyrepro = if_else(clutches == "0", "0", "1"))
LA_infected$anyrepro <- as.numeric(LA_infected$anyrepro)
# Adding a column for log spores
LA_infected$logspores <- log10(LA_infected$spores_tot)
LA_infected$lnspores <- log(LA_infected$spores_tot)LA_infected <- subset(LA_infected, host_time_point != "4")str(LA_infected)## tibble[,16] [259 × 16] (S3: tbl_df/tbl/data.frame)
## $ clone : num [1:259] 343 17 304 304 324 311 323 29 333 29 ...
## $ block : num [1:259] 1 2 1 1 2 1 1 1 2 1 ...
## $ rep : chr [1:259] "R01" "R2" "R03" "R02" ...
## $ treatment : chr [1:259] "B" "C" "B" "C" ...
## $ death_date : Date[1:259], format: "2019-02-18" "2019-02-22" ...
## $ infection : chr [1:259] NA NA NA NA ...
## $ birth_date : Date[1:259], format: "2019-02-09" "2019-02-14" ...
## $ host_time_point: num [1:259] 3 1 3 3 3 3 3 1 3 1 ...
## $ para_time_point: chr [1:259] "3" "none" "3" "none" ...
## $ lifespan : num [1:259] 9 8 15 15 12 22 22 22 17 23 ...
## $ avg : num [1:259] 6.75 0 0.25 0 0 ...
## $ spores_tot : num [1:259] 6750 0 250 0 0 ...
## $ clutches : num [1:259] 0 0 4 2 0 0 0 2 0 1 ...
## $ anyrepro : num [1:259] 0 0 1 1 0 0 0 1 0 1 ...
## $ logspores : num [1:259] 3.83 -Inf 2.4 -Inf -Inf ...
## $ lnspores : num [1:259] 8.82 -Inf 5.52 -Inf -Inf ...LA_infected$clone <- as.factor(as.character(LA_infected$clone))
# get mean lifespan for each clone*treatment
stats_lifesp <- LA_infected %>%
group_by(host_time_point, para_time_point, clone) %>%
summarise(mn_lifespan = mean(lifespan)) ## `summarise()` has grouped output by 'host_time_point', 'para_time_point'. You can override using the `.groups` argument.# for selecting contemporaneous pairs of hosts/parasites
stats_lifesp$host_time_point <- as.character(stats_lifesp$host_time_point)
stats_lifesp$para_time_point <- as.character(stats_lifesp$para_time_point)
LA_inf <- stats_lifesp %>% # select non-controls
filter(para_time_point != "none")
LA_control <- stats_lifesp %>% # select controls
filter(host_time_point != "4") %>%
filter(para_time_point == "none") %>%
ungroup() %>%
select(-para_time_point)
colnames(LA_control)[3] <- "control_lifespan"
# calculate average lifespan for all controls together (grouped by time point)
LA_control_avg <- LA_control %>%
group_by(host_time_point) %>%
summarise(mean_life = mean(control_lifespan))
# turn into an unamed matrix so we can easily extract values later
mean_mat <- unname(as.matrix(LA_control_avg))
# rejoin infected and control dfs
LA_rel_vir_lifespan <- full_join(LA_inf, LA_control)## Joining, by = c("host_time_point", "clone")LA_rel_vir_lifespan$host_time_point <- as.factor(LA_rel_vir_lifespan$host_time_point)
# make a separate file to use later for infected v. control comparison
LA_rel_vir_lifespan_infvcontrol <- full_join(LA_inf, LA_control)## Joining, by = c("host_time_point", "clone")LA_rel_vir_lifespan_infvcontrol$host_time_point <- as.factor(LA_rel_vir_lifespan_infvcontrol$host_time_point)
# Replace NAs with the average control lifespan for that group
# do this b/c didn't have controls for each clone
LA_rel_vir_lifespan$control_lifespan[is.na(LA_rel_vir_lifespan$control_lifespan) &
LA_rel_vir_lifespan$host_time_point == "1"] <- mean_mat[1,2] # means
LA_rel_vir_lifespan$control_lifespan[is.na(LA_rel_vir_lifespan$control_lifespan) &
LA_rel_vir_lifespan$host_time_point == "2"] <- mean_mat[2,2]
LA_rel_vir_lifespan$control_lifespan[is.na(LA_rel_vir_lifespan$control_lifespan) &
LA_rel_vir_lifespan$host_time_point == "3"] <- mean_mat[3,2]
# calculate relative virulence using mean and median control lifespans
LA_rel_vir_lifespan$control_lifespan <- as.numeric(LA_rel_vir_lifespan$control_lifespan)
LA_rel_vir_lifespan <- LA_rel_vir_lifespan %>%
mutate(rel_lifespan_mn = mn_lifespan/control_lifespan)
LA_rel_vir_lifespan <- as.data.frame(LA_rel_vir_lifespan)
str(LA_rel_vir_lifespan)## 'data.frame': 41 obs. of 6 variables:
## $ host_time_point : Factor w/ 3 levels "1","2","3": 1 1 1 1 2 2 2 2 2 2 ...
## $ para_time_point : chr "1" "1" "1" "1" ...
## $ clone : Factor w/ 28 levels "17","211","22",..: 1 3 13 16 2 4 5 6 7 8 ...
## $ mn_lifespan : num 33 38.7 27.1 42 35.8 ...
## $ control_lifespan: num 46 37.7 58.8 26 54.9 ...
## $ rel_lifespan_mn : num 0.717 1.027 0.461 1.615 0.651 ...str(LA_infected)## tibble[,16] [259 × 16] (S3: tbl_df/tbl/data.frame)
## $ clone : Factor w/ 28 levels "17","211","22",..: 28 1 14 14 21 17 20 13 24 13 ...
## $ block : num [1:259] 1 2 1 1 2 1 1 1 2 1 ...
## $ rep : chr [1:259] "R01" "R2" "R03" "R02" ...
## $ treatment : chr [1:259] "B" "C" "B" "C" ...
## $ death_date : Date[1:259], format: "2019-02-18" "2019-02-22" ...
## $ infection : chr [1:259] NA NA NA NA ...
## $ birth_date : Date[1:259], format: "2019-02-09" "2019-02-14" ...
## $ host_time_point: num [1:259] 3 1 3 3 3 3 3 1 3 1 ...
## $ para_time_point: chr [1:259] "3" "none" "3" "none" ...
## $ lifespan : num [1:259] 9 8 15 15 12 22 22 22 17 23 ...
## $ avg : num [1:259] 6.75 0 0.25 0 0 ...
## $ spores_tot : num [1:259] 6750 0 250 0 0 ...
## $ clutches : num [1:259] 0 0 4 2 0 0 0 2 0 1 ...
## $ anyrepro : num [1:259] 0 0 1 1 0 0 0 1 0 1 ...
## $ logspores : num [1:259] 3.83 -Inf 2.4 -Inf -Inf ...
## $ lnspores : num [1:259] 8.82 -Inf 5.52 -Inf -Inf ...# get mean number clutches for each clone*treatment
stats_clutches <- LA_infected %>%
group_by(host_time_point, para_time_point, clone) %>%
summarise(mn_clutches = mean(clutches)) ## `summarise()` has grouped output by 'host_time_point', 'para_time_point'. You can override using the `.groups` argument.# for selecting contemporaneous pairs of hosts/parasites
stats_clutches$host_time_point <- as.character(stats_clutches$host_time_point)
stats_clutches$para_time_point <- as.character(stats_clutches$para_time_point)
LA_inf <- stats_clutches %>% # select non-controls
filter(para_time_point != "none")
LA_control <- stats_clutches %>% # select controls
filter(host_time_point != "4") %>%
filter(para_time_point == "none") %>%
ungroup() %>%
select(-para_time_point)
colnames(LA_control)[3] <- "control_clutches"
# calculate average no. clutches for all controls together (grouped by time point)
LA_control_avg <- LA_control %>%
group_by(host_time_point) %>%
summarise(mean_clutches = mean(control_clutches))
# turn into an unamed matrix so we can easily extract values later
mean_mat <- unname(as.matrix(LA_control_avg))
# rejoin infected and control dfs
LA_rel_vir_clutches <- full_join(LA_inf, LA_control)## Joining, by = c("host_time_point", "clone")LA_rel_vir_clutches$host_time_point <- as.factor(LA_rel_vir_clutches$host_time_point)
# make a separate file to use later for infected v. control comparison
LA_rel_vir_clutches_infvcontrol <- full_join(LA_inf, LA_control)## Joining, by = c("host_time_point", "clone")LA_rel_vir_clutches_infvcontrol$host_time_point <- as.factor(LA_rel_vir_clutches_infvcontrol$host_time_point)
# Replace NAs with the average control number of clutches for that group
# do this b/c didn't have controls for each clone
LA_rel_vir_clutches$control_clutches[is.na(LA_rel_vir_clutches$control_clutches) &
LA_rel_vir_clutches$host_time_point == "1"] <- mean_mat[1,2] # means
LA_rel_vir_clutches$control_clutches[is.na(LA_rel_vir_clutches$control_clutches) &
LA_rel_vir_clutches$host_time_point == "2"] <- mean_mat[2,2]
LA_rel_vir_clutches$control_clutches[is.na(LA_rel_vir_clutches$control_clutches) &
LA_rel_vir_clutches$host_time_point == "3"] <- mean_mat[3,2]
# calculate relative virulence using mean and median control clutches
LA_rel_vir_clutches$control_clutches <- as.numeric(LA_rel_vir_clutches$control_clutches)
LA_rel_vir_clutches <- LA_rel_vir_clutches %>%
mutate(rel_clutches_mn = mn_clutches/control_clutches)
LA_rel_vir_clutches <- as.data.frame(LA_rel_vir_clutches)
str(LA_rel_vir_clutches)## 'data.frame': 41 obs. of 6 variables:
## $ host_time_point : Factor w/ 3 levels "1","2","3": 1 1 1 1 2 2 2 2 2 2 ...
## $ para_time_point : chr "1" "1" "1" "1" ...
## $ clone : Factor w/ 28 levels "17","211","22",..: 1 3 13 16 2 4 5 6 7 8 ...
## $ mn_clutches : num 0 5.333 0.867 0 0.25 ...
## $ control_clutches: num 11.3 9 19.4 1 16 ...
## $ rel_clutches_mn : num 0 0.5926 0.0447 0 0.0156 ...LA_rel_vir_2 <- full_join(LA_rel_vir_lifespan, LA_rel_vir_clutches)## Joining, by = c("host_time_point", "para_time_point", "clone")str(LA_rel_vir_2)## 'data.frame': 41 obs. of 9 variables:
## $ host_time_point : Factor w/ 3 levels "1","2","3": 1 1 1 1 2 2 2 2 2 2 ...
## $ para_time_point : chr "1" "1" "1" "1" ...
## $ clone : Factor w/ 28 levels "17","211","22",..: 1 3 13 16 2 4 5 6 7 8 ...
## $ mn_lifespan : num 33 38.7 27.1 42 35.8 ...
## $ control_lifespan: num 46 37.7 58.8 26 54.9 ...
## $ rel_lifespan_mn : num 0.717 1.027 0.461 1.615 0.651 ...
## $ mn_clutches : num 0 5.333 0.867 0 0.25 ...
## $ control_clutches: num 11.3 9 19.4 1 16 ...
## $ rel_clutches_mn : num 0 0.5926 0.0447 0 0.0156 ...str(LA_infected)## tibble[,16] [259 × 16] (S3: tbl_df/tbl/data.frame)
## $ clone : Factor w/ 28 levels "17","211","22",..: 28 1 14 14 21 17 20 13 24 13 ...
## $ block : num [1:259] 1 2 1 1 2 1 1 1 2 1 ...
## $ rep : chr [1:259] "R01" "R2" "R03" "R02" ...
## $ treatment : chr [1:259] "B" "C" "B" "C" ...
## $ death_date : Date[1:259], format: "2019-02-18" "2019-02-22" ...
## $ infection : chr [1:259] NA NA NA NA ...
## $ birth_date : Date[1:259], format: "2019-02-09" "2019-02-14" ...
## $ host_time_point: num [1:259] 3 1 3 3 3 3 3 1 3 1 ...
## $ para_time_point: chr [1:259] "3" "none" "3" "none" ...
## $ lifespan : num [1:259] 9 8 15 15 12 22 22 22 17 23 ...
## $ avg : num [1:259] 6.75 0 0.25 0 0 ...
## $ spores_tot : num [1:259] 6750 0 250 0 0 ...
## $ clutches : num [1:259] 0 0 4 2 0 0 0 2 0 1 ...
## $ anyrepro : num [1:259] 0 0 1 1 0 0 0 1 0 1 ...
## $ logspores : num [1:259] 3.83 -Inf 2.4 -Inf -Inf ...
## $ lnspores : num [1:259] 8.82 -Inf 5.52 -Inf -Inf ...# get mean number clutches for each clone*treatment
stats_spores <- LA_infected %>%
group_by(host_time_point, para_time_point, clone) %>%
summarise(mn_spores = mean(spores_tot)) ## `summarise()` has grouped output by 'host_time_point', 'para_time_point'. You can override using the `.groups` argument.# for selecting contemporaneous pairs of hosts/parasites
stats_spores$host_time_point <- as.character(stats_spores$host_time_point)
stats_spores$para_time_point <- as.character(stats_spores$para_time_point)
LA_inf <- stats_spores %>% # select non-controls
filter(para_time_point != "none")
# join spore data with earlier calculations
LA_means <- full_join(LA_rel_vir_2, LA_inf)## Joining, by = c("host_time_point", "para_time_point", "clone")LA_means$host_time_point <- as.factor(LA_means$host_time_point)
str(LA_means)## 'data.frame': 41 obs. of 10 variables:
## $ host_time_point : Factor w/ 3 levels "1","2","3": 1 1 1 1 2 2 2 2 2 2 ...
## $ para_time_point : chr "1" "1" "1" "1" ...
## $ clone : Factor w/ 28 levels "17","211","22",..: 1 3 13 16 2 4 5 6 7 8 ...
## $ mn_lifespan : num 33 38.7 27.1 42 35.8 ...
## $ control_lifespan: num 46 37.7 58.8 26 54.9 ...
## $ rel_lifespan_mn : num 0.717 1.027 0.461 1.615 0.651 ...
## $ mn_clutches : num 0 5.333 0.867 0 0.25 ...
## $ control_clutches: num 11.3 9 19.4 1 16 ...
## $ rel_clutches_mn : num 0 0.5926 0.0447 0 0.0156 ...
## $ mn_spores : num 41000 46750 166750 263750 260625 ...Infected vs. uninfected comparison
Let’s start by looking at lifespan of control/unexposed animals vs. infected animals. Based on what we know about the system, these may not be very different.
# Making a long data set with the uninfected & infected animals to compare lifespan
LA_rel_vir_lifespan_infvcontrol <- LA_rel_vir_lifespan_infvcontrol %>%
rename(Infected = mn_lifespan)
LA_rel_vir_lifespan_infvcontrol <- LA_rel_vir_lifespan_infvcontrol %>%
rename(Unexposed = control_lifespan)
LA_rel_vir_lifespan_infvcontrol_long <- LA_rel_vir_lifespan_infvcontrol %>%
gather(infvcontrol, lifespan, Infected:Unexposed)
LA_rel_vir_lifespan_infvcontrol_long <- na.omit(LA_rel_vir_lifespan_infvcontrol_long)
# Now do the same for the reproduction data
LA_rel_vir_clutches_infvcontrol <- LA_rel_vir_clutches_infvcontrol %>%
rename(Infected = mn_clutches)
LA_rel_vir_clutches_infvcontrol <- LA_rel_vir_clutches_infvcontrol %>%
rename(Unexposed = control_clutches)
LA_rel_vir_clutches_infvcontrol_long <- LA_rel_vir_clutches_infvcontrol %>%
gather(infvcontrol, clutches, Infected:Unexposed)
LA_rel_vir_clutches_infvcontrol_long <- na.omit(LA_rel_vir_clutches_infvcontrol_long)lifespaninfvcontrol <- ggplot(LA_rel_vir_lifespan_infvcontrol_long,
aes(x=infvcontrol, y=lifespan)) +
geom_violin() +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, color = '#5c1a33') +
xlab("Infected or unexposed") +
ylab("Mean lifespan \n(days)") +
theme_cowplot()
lifespaninfvcontrol
infvcontrollifespanmodel <- glm(lifespan ~ infvcontrol,
family = "quasipoisson",
data = LA_rel_vir_lifespan_infvcontrol_long)
plot(infvcontrollifespanmodel) 
summary(infvcontrollifespanmodel)##
## Call:
## glm(formula = lifespan ~ infvcontrol, family = "quasipoisson",
## data = LA_rel_vir_lifespan_infvcontrol_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2551 -0.3763 -0.0037 0.5306 1.7622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.55025 0.02475 143.435 < 2e-16 ***
## infvcontrolUnexposed 0.28494 0.04235 6.729 1.05e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.8746756)
##
## Null deviance: 87.448 on 56 degrees of freedom
## Residual deviance: 49.126 on 55 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4tapply(LA_rel_vir_lifespan_infvcontrol_long$lifespan, LA_rel_vir_lifespan_infvcontrol_long$infvcontrol, mean)## Infected Unexposed
## 34.82208 46.30208Fecundity
Expectation: infected ones should have many fewer clutches.
# making a plot comparing exposed vs. unexposed
fecundityinfvcontrol <- ggplot(LA_rel_vir_clutches_infvcontrol_long,
aes(x=infvcontrol, y=clutches)) +
geom_violin() +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, color = '#5c1a33') +
xlab("Infected or unexposed") +ylab("Mean # clutches") +
theme_cowplot()
fecundityinfvcontrol
LA_rel_vir_clutches_infvcontrol_long$log_clutches <- log((LA_rel_vir_clutches_infvcontrol_long$clutches+1))
infvcontrolrepromodel <- glm(log_clutches ~ infvcontrol,
family = "gaussian",
data = LA_rel_vir_clutches_infvcontrol_long)
plot(infvcontrolrepromodel) 
summary(infvcontrolrepromodel)##
## Call:
## glm(formula = log_clutches ~ infvcontrol, family = "gaussian",
## data = LA_rel_vir_clutches_infvcontrol_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8405 -0.2869 -0.1015 0.2636 1.5457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.46921 0.08332 5.632 6.29e-07 ***
## infvcontrolUnexposed 2.06442 0.15726 13.127 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2846221)
##
## Null deviance: 64.702 on 56 degrees of freedom
## Residual deviance: 15.654 on 55 degrees of freedom
## AIC: 94.097
##
## Number of Fisher Scoring iterations: 2tapply(LA_rel_vir_clutches_infvcontrol_long$clutches, LA_rel_vir_clutches_infvcontrol_long$infvcontrol, mean)## Infected Unexposed
## 0.9101239 12.7895833Let’s arrange the lifespan & fecundity plots into a single figure:
infvcontrolplot <- plot_grid(lifespaninfvcontrol, fecundityinfvcontrol, labels = "auto", ncol = 1, align = "v")
infvcontrolplot
ggsave(here("figures", "infvcontrolplot.pdf"), infvcontrolplot, units = "in", width = 5, height = 8, dpi = 300)
ggsave(here("figures", "infvcontrolplot.jpg"), infvcontrolplot, units = "in", width = 5, height = 8, dpi = 300)Same time comparison
Let’s first look at the lifespan of animals that were exposed to spores from the same time point
LA_means_sametime <- subset(LA_means, host_time_point == para_time_point)Lifespan
# plotting animals exposed to parasite from the same time point
lifespansametime <- ggplot(LA_means_sametime,
aes(x=host_time_point, y=mn_lifespan, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
xlab("Host time point") +ylab("Mean lifespan \n(days)") +
theme_cowplot()
#colors from https://github.com/ciannabp/inauguration/blob/main/R/colors.R
lifespansametime
mod1a <- glm(mn_lifespan ~ host_time_point,
family = "quasipoisson",
data = LA_means_sametime)
plot(mod1a) 
summary(mod1a)##
## Call:
## glm(formula = mn_lifespan ~ host_time_point, family = "quasipoisson",
## data = LA_means_sametime)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6730 -0.3918 -0.1085 0.3645 1.5834
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.561e+00 7.029e-02 50.661 <2e-16 ***
## host_time_point2 3.363e-02 8.278e-02 0.406 0.688
## host_time_point3 -7.978e-05 8.038e-02 -0.001 0.999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.6956699)
##
## Null deviance: 16.829 on 26 degrees of freedom
## Residual deviance: 16.573 on 24 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4Lifespan
# plotting animals exposed to parasite from the same time point
rellifespansametime <- ggplot(LA_means_sametime,
aes(x=host_time_point, y=rel_lifespan_mn, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
# ggtitle("Lifespan When Exposed to Parasites from the \nSame Time Point") +
xlab("Host time point") +ylab("Relative host lifespan") +
# labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
#colors from https://github.com/ciannabp/inauguration/blob/main/R/colors.R
rellifespansametime
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod1arel <- glm(rel_lifespan_mn ~ host_time_point,
family = "quasipoisson",
data = LA_means_sametime)
plot(mod1arel) 
summary(mod1arel)##
## Call:
## glm(formula = rel_lifespan_mn ~ host_time_point, family = "quasipoisson",
## data = LA_means_sametime)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.56217 -0.08694 -0.01690 0.10842 0.61409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04584 0.11057 -0.415 0.6821
## host_time_point2 -0.36181 0.13874 -2.608 0.0154 *
## host_time_point3 -0.17559 0.12927 -1.358 0.1870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.0467133)
##
## Null deviance: 1.4387 on 26 degrees of freedom
## Residual deviance: 1.1079 on 24 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4Reproduction
# plotting animals exposed to parasite from the same time point
reprosametime <- ggplot(LA_means_sametime,
aes(x=host_time_point, y=mn_clutches, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
# ggtitle("Lifespan When Exposed to Parasites from the \nSame Time Point") +
xlab("Host time point") +ylab("Mean # clutches") +
# labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
reprosametime
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_sametime$log_mn_clutches <- log((LA_means_sametime$mn_clutches+1))
mod1b <- glm(log_mn_clutches ~ host_time_point,
family = "gaussian",
data = LA_means_sametime)
plot(mod1b) 
summary(mod1b)##
## Call:
## glm(formula = log_mn_clutches ~ host_time_point, family = "gaussian",
## data = LA_means_sametime)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6175 -0.4707 -0.1030 0.1461 1.3115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.61750 0.31082 1.987 0.0585 .
## host_time_point2 0.08587 0.36776 0.233 0.8174
## host_time_point3 -0.14681 0.35543 -0.413 0.6832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.3864272)
##
## Null deviance: 9.5874 on 26 degrees of freedom
## Residual deviance: 9.2743 on 24 degrees of freedom
## AIC: 55.771
##
## Number of Fisher Scoring iterations: 2Relative reproduction
# plotting animals exposed to parasite from the same time point
relreprosametime <- ggplot(LA_means_sametime,
aes(x=host_time_point, y=rel_clutches_mn, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
xlab("Host time point") +ylab("Relative # clutches") +
# labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
relreprosametime
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_sametime$log_rel_clutches_mn <- log((LA_means_sametime$rel_clutches_mn+1))
mod1brel <- glm(log_rel_clutches_mn ~ host_time_point,
family = "gaussian",
data = LA_means_sametime)
plot(mod1brel) 
summary(mod1brel)##
## Call:
## glm(formula = log_rel_clutches_mn ~ host_time_point, family = "gaussian",
## data = LA_means_sametime)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.12727 -0.07330 -0.04258 0.01503 0.33810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12727 0.05986 2.126 0.044 *
## host_time_point2 -0.03734 0.07083 -0.527 0.603
## host_time_point3 -0.06881 0.06846 -1.005 0.325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01433495)
##
## Null deviance: 0.35999 on 26 degrees of freedom
## Residual deviance: 0.34404 on 24 degrees of freedom
## AIC: -33.174
##
## Number of Fisher Scoring iterations: 2Spores
# plotting animals exposed to parasite from the same time point
sporessametime <- ggplot(LA_means_sametime,
aes(x=host_time_point, y=mn_spores, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
# ggtitle("Lifespan When Exposed to Parasites from the \nSame Time Point") +
xlab("Host time point") +ylab("Mean # spores \nper infected host") +
# labs(color= "Parasite time point") + labs(fill="Parasite time point") +
scale_y_log10() +
theme_cowplot()
sporessametime
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_sametime$log_mn_spores <- log(LA_means_sametime$mn_spores)
mod1c <- glm(log_mn_spores ~ host_time_point,
family = "gaussian",
data = LA_means_sametime)
plot(mod1c) 
summary(mod1c)##
## Call:
## glm(formula = log_mn_spores ~ host_time_point, family = "gaussian",
## data = LA_means_sametime)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4.4087 -0.5554 0.3632 0.6789 1.4764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.47023 0.60563 18.939 6.15e-16 ***
## host_time_point2 -0.15376 0.71660 -0.215 0.832
## host_time_point3 -0.07792 0.69257 -0.113 0.911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.467173)
##
## Null deviance: 35.287 on 26 degrees of freedom
## Residual deviance: 35.212 on 24 degrees of freedom
## AIC: 91.793
##
## Number of Fisher Scoring iterations: 2Parasite growth rate
LA_means_sametime$paragrowth <- (LA_means_sametime$mn_spores)/(LA_means_sametime$mn_lifespan)
paragrowthsametime <- ggplot(LA_means_sametime,
aes(x=host_time_point, y=paragrowth, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
xlab("Host time point") +ylab("Parasite growth rate \n(spores per day)") +
# labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
paragrowthsametime
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod1d <- glm(paragrowth ~ host_time_point,
family = "gaussian",
data = LA_means_sametime)
plot(mod1d) 
summary(mod1d)##
## Call:
## glm(formula = paragrowth ~ host_time_point, family = "gaussian",
## data = LA_means_sametime)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -4242.4 -1856.4 -411.4 1778.1 6216.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3719.2 1268.5 2.932 0.00729 **
## host_time_point2 553.5 1500.9 0.369 0.71552
## host_time_point3 -673.7 1450.6 -0.464 0.64655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6436483)
##
## Null deviance: 163054339 on 26 degrees of freedom
## Residual deviance: 154475581 on 24 degrees of freedom
## AIC: 504.73
##
## Number of Fisher Scoring iterations: 2Combined figure
sametimeplot <- plot_grid(lifespansametime, rellifespansametime, reprosametime, relreprosametime, sporessametime, paragrowthsametime, labels = "auto", ncol = 2, align = "v")
sametimeplot
ggsave(here("figures", "sametimeplot.pdf"), sametimeplot, units = "in", width = 12, height = 7, dpi = 300)
ggsave(here("figures", "sametimeplot.jpg"), sametimeplot, units = "in", width = 12, height = 7, dpi = 300)Comparing lifespan vs. spore yield
lifespansporeyieldplot <- ggplot(LA_means_sametime,
aes(x=mn_lifespan, y=mn_spores, fill=para_time_point)) +
geom_jitter(shape=16, position=position_jitter(width=0.1, height=0), alpha = 0.8, aes(colour=para_time_point)) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
#ggtitle("Spore Yield When Exposed to Parasites from the \nSame Time Point") +
xlab("Mean lifespan (days)") +ylab("Mean spore yield \n(spores per host)") +
labs(color= "Parasite Time Point") + labs(fill="Parasite Time Point") +
scale_y_log10() +
geom_smooth(method='lm') +
theme_cowplot()
lifespansporeyieldplot## `geom_smooth()` using formula 'y ~ x'
ggsave(here("figures", "lifespansporeyieldplot.pdf"), lifespansporeyieldplot, units = "in", width = 6, height = 3, dpi = 300)## `geom_smooth()` using formula 'y ~ x'ggsave(here("figures", "lifespansporeyieldplot.jpg"), lifespansporeyieldplot, units = "in", width = 6, height = 3, dpi = 300)## `geom_smooth()` using formula 'y ~ x'Considering tolerance
toleranceplotA <- ggplot(LA_means_sametime,
aes(x=mn_spores, y=mn_lifespan, fill=para_time_point)) +
geom_jitter(shape=16, position=position_jitter(width=0.1, height=0), alpha = 0.8, aes(colour=para_time_point)) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
#ggtitle("Spore Yield When Exposed to Parasites from the \nSame Time Point") +
xlab("Spore yield") +ylab("Lifespan") +
labs(color= "Parasite Time Point") + labs(fill="Parasite Time Point") +
scale_x_log10() +
geom_smooth(method='lm') +
theme_cowplot()
#toleranceplotA
#ggsave(here("figures", "lifespansporeyieldplot.pdf"), lifespansporeyieldplot, units = "in", width = 6, height = 3, dpi = 300)
#ggsave(here("figures", "lifespansporeyieldplot.jpg"), lifespansporeyieldplot, units = "in", width = 6, height = 3, dpi = 300)
toleranceplotB <- ggplot(LA_means_sametime,
aes(x=mn_spores, y=mn_clutches, fill=para_time_point)) +
geom_jitter(shape=16, position=position_jitter(width=0.1, height=0), alpha = 0.8, aes(colour=para_time_point)) +
scale_fill_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
scale_color_manual(values=c("#5445b1", "#749dae", "#f3c483")) +
#ggtitle("Spore Yield When Exposed to Parasites from the \nSame Time Point") +
xlab("Spore yield") +ylab("Clutches") +
labs(color= "Parasite Time Point") + labs(fill="Parasite Time Point") +
scale_x_log10() +
geom_smooth(method='lm') +
theme_cowplot()
#toleranceplotB
toleranceplot <- plot_grid(toleranceplotA, toleranceplotB, labels = "auto", ncol = 1, align = "v")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'toleranceplot
LA_means_sametime_time3 <- subset(LA_means_sametime, para_time_point == '3')
LA_means_sametime_time3$log_spores <- log(LA_means_sametime_time3$mn_spores)
cor.test(LA_means_sametime_time3$log_spores, LA_means_sametime_time3$mn_clutches, method="pearson")##
## Pearson's product-moment correlation
##
## data: LA_means_sametime_time3$log_spores and LA_means_sametime_time3$mn_clutches
## t = -0.71063, df = 11, p-value = 0.4921
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6817900 0.3860405
## sample estimates:
## cor
## -0.2095076cor.test(LA_means_sametime_time3$log_spores, LA_means_sametime_time3$mn_lifespan, method="pearson")##
## Pearson's product-moment correlation
##
## data: LA_means_sametime_time3$log_spores and LA_means_sametime_time3$mn_lifespan
## t = 1.6013, df = 11, p-value = 0.1376
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1527975 0.7952616
## sample estimates:
## cor
## 0.4347927Parasite evolution comparison
Comparing hosts from time 1 and hosts from time 3 that were exposed to parasites from time 3
LA_means_paraevol <- subset(LA_means, host_time_point == 3)Lifespan
lifespanparaevol <- ggplot(LA_means_paraevol,
aes(x=para_time_point, y=mn_lifespan, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#C0C0C0", "#f3c483")) +
scale_color_manual(values=c("#C0C0C0", "#f3c483")) +
xlab("Parasite time point") +ylab("Mean lifespan \n(days)") +
labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
lifespanparaevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod2a <- glm(mn_lifespan ~ para_time_point,
family = "quasipoisson",
data = LA_means_paraevol)
plot(mod2a) 
summary(mod2a)##
## Call:
## glm(formula = mn_lifespan ~ para_time_point, family = "quasipoisson",
## data = LA_means_paraevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.67302 -0.40054 -0.03327 0.30336 1.58336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.50363 0.03222 108.725 <2e-16 ***
## para_time_point3 0.05734 0.04576 1.253 0.222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.4831798)
##
## Null deviance: 12.774 on 26 degrees of freedom
## Residual deviance: 12.016 on 25 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4Relative lifespan
rellifespanparaevol <- ggplot(LA_means_paraevol,
aes(x=para_time_point, y=rel_lifespan_mn, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#C0C0C0", "#f3c483")) +
scale_color_manual(values=c("#C0C0C0", "#f3c483")) +
xlab("Parasite time point") +ylab("Relative host lifespan") +
labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
rellifespanparaevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod2arel <- glm(rel_lifespan_mn ~ para_time_point,
family = "quasipoisson",
data = LA_means_paraevol)
plot(mod2arel) 
summary(mod2arel)##
## Call:
## glm(formula = rel_lifespan_mn ~ para_time_point, family = "quasipoisson",
## data = LA_means_paraevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.25553 -0.05754 -0.01358 0.04401 0.21570
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.28111 0.03458 -8.130 1.75e-08 ***
## para_time_point3 0.05969 0.04908 1.216 0.235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.01263767)
##
## Null deviance: 0.33503 on 26 degrees of freedom
## Residual deviance: 0.31635 on 25 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4Reproduction
reproparaevol <- ggplot(LA_means_paraevol,
aes(x=para_time_point, y=mn_clutches, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#C0C0C0", "#f3c483")) +
scale_color_manual(values=c("#C0C0C0", "#f3c483")) +
xlab("Parasite time point") +ylab("Mean # clutches") +
labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
reproparaevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_paraevol$log_mn_clutches <- log((LA_means_paraevol$mn_clutches+1))
mod2b <- glm(log_mn_clutches ~ para_time_point,
family = "gaussian",
data = LA_means_paraevol)
plot(mod2b) 
summary(mod2b)##
## Call:
## glm(formula = log_mn_clutches ~ para_time_point, family = "gaussian",
## data = LA_means_paraevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.47069 -0.25822 -0.03508 0.12837 1.03339
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2582 0.1019 2.535 0.0179 *
## para_time_point3 0.2125 0.1468 1.447 0.1603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1452936)
##
## Null deviance: 3.9366 on 26 degrees of freedom
## Residual deviance: 3.6323 on 25 degrees of freedom
## AIC: 28.462
##
## Number of Fisher Scoring iterations: 2Relative reproduction
relreproparaevol <- ggplot(LA_means_paraevol,
aes(x=para_time_point, y=rel_clutches_mn, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#C0C0C0", "#f3c483")) +
scale_color_manual(values=c("#C0C0C0", "#f3c483")) +
xlab("Parasite time point") +ylab("Relative # clutches") +
# labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
relreproparaevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_paraevol$log_rel_clutches_mn <- log((LA_means_paraevol$rel_clutches_mn+1))
mod2brel <- glm(log_rel_clutches_mn ~ para_time_point,
family = "gaussian",
data = LA_means_paraevol)
plot(mod2brel) 
summary(mod2brel)##
## Call:
## glm(formula = log_rel_clutches_mn ~ para_time_point, family = "gaussian",
## data = LA_means_paraevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.05845 -0.02668 -0.01157 0.01204 0.14514
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02668 0.01361 1.96 0.0612 .
## para_time_point3 0.03178 0.01961 1.62 0.1178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.002593259)
##
## Null deviance: 0.071638 on 26 degrees of freedom
## Residual deviance: 0.064831 on 25 degrees of freedom
## AIC: -80.236
##
## Number of Fisher Scoring iterations: 2Spores
sporesparaevol <- ggplot(LA_means_paraevol,
aes(x=para_time_point, y=mn_spores, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#C0C0C0", "#f3c483")) +
scale_color_manual(values=c("#C0C0C0", "#f3c483")) +
xlab("Parasite time point") +ylab("Mean # spores \nper infected host") +
labs(color= "Parasite time point") + labs(fill="Parasite time point") +
scale_y_log10() +
theme_cowplot()
sporesparaevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_paraevol$log_mn_spores <- log(LA_means_paraevol$mn_spores)
mod2c <- glm(log_mn_spores ~ para_time_point,
family = "gaussian",
data = LA_means_paraevol)
plot(mod2c) 
summary(mod2c)##
## Call:
## glm(formula = log_mn_spores ~ para_time_point, family = "gaussian",
## data = LA_means_paraevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1748 -0.4538 0.1635 0.4625 0.8994
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.8023 0.1643 71.817 <2e-16 ***
## para_time_point3 -0.4099 0.2368 -1.731 0.0958 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.3780972)
##
## Null deviance: 10.5853 on 26 degrees of freedom
## Residual deviance: 9.4524 on 25 degrees of freedom
## AIC: 54.284
##
## Number of Fisher Scoring iterations: 2Parasite growth rate
LA_means_paraevol$paragrowth <- (LA_means_paraevol$mn_spores)/(LA_means_paraevol$mn_lifespan)
paragrowthparaevol <- ggplot(LA_means_paraevol,
aes(x=para_time_point, y=paragrowth, fill=para_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#C0C0C0", "#f3c483")) +
scale_color_manual(values=c("#C0C0C0", "#f3c483")) +
xlab("Parasite time point") +ylab("Parasite growth rate \n(spores per day)") +
labs(color= "Parasite time point") + labs(fill="Parasite time point") +
theme_cowplot()
paragrowthparaevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod2d <- glm(paragrowth ~ para_time_point,
family = "gaussian",
data = LA_means_paraevol)
plot(mod2d) 
summary(mod2d)##
## Call:
## glm(formula = paragrowth ~ para_time_point, family = "gaussian",
## data = LA_means_paraevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3060.5 -1444.9 126.3 1405.7 4602.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4482.9 511.7 8.762 4.3e-09 ***
## para_time_point3 -1437.4 737.4 -1.949 0.0626 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3665074)
##
## Null deviance: 105553897 on 26 degrees of freedom
## Residual deviance: 91626856 on 25 degrees of freedom
## AIC: 488.63
##
## Number of Fisher Scoring iterations: 2Combined figure
paraevolplot <- plot_grid(lifespanparaevol, rellifespanparaevol, reproparaevol, relreproparaevol, sporesparaevol, paragrowthparaevol, labels = "auto", ncol = 2, align = "v")
paraevolplot
ggsave(here("figures", "paraevolplot.pdf"), paraevolplot, units = "in", width = 10, height = 7, dpi = 300)
ggsave(here("figures", "paraevolplot.jpg"), paraevolplot, units = "in", width = 10, height = 7, dpi = 300)Host evolution comparison
Comparing hosts from time 1 and hosts from time 3 that were exposed to parasites from time 3
LA_means_hostevol <- subset(LA_means, para_time_point == 1)Lifespan
lifespanhostevol <- ggplot(LA_means_hostevol,
aes(x=host_time_point, y=mn_lifespan, fill=host_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#808080")) +
scale_color_manual(values=c("#5445b1", "#808080")) +
xlab("Host time point") +ylab("Mean lifespan \n(days)") +
labs(color= "Host time point") + labs(fill="Host time point") +
theme_cowplot()
lifespanhostevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod3a <- glm(mn_lifespan ~ host_time_point,
family = "quasipoisson",
data = LA_means_hostevol)
plot(mod3a) 
summary(mod3a)##
## Call:
## glm(formula = mn_lifespan ~ host_time_point, family = "quasipoisson",
## data = LA_means_hostevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.41726 -0.38792 0.03013 0.35122 1.11192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.56105 0.05437 65.497 <2e-16 ***
## host_time_point3 -0.05742 0.06205 -0.925 0.369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 0.4162173)
##
## Null deviance: 7.1131 on 17 degrees of freedom
## Residual deviance: 6.7604 on 16 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4Relative lifespan
rellifespanhostevol <- ggplot(LA_means_hostevol,
aes(x=host_time_point, y=rel_lifespan_mn, fill=host_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#808080")) +
scale_color_manual(values=c("#5445b1", "#808080")) +
xlab("Host time point") +ylab("Relative host lifespan") +
labs(color= "Host time point") + labs(fill="Host time point") +
theme_cowplot()
rellifespanhostevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod3arel <- glm(rel_lifespan_mn ~ host_time_point,
family = "quasipoisson",
data = LA_means_hostevol)
plot(mod3arel) 
summary(mod3arel)##
## Call:
## glm(formula = rel_lifespan_mn ~ host_time_point, family = "quasipoisson",
## data = LA_means_hostevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.56217 -0.05907 -0.01070 0.04401 0.61409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04584 0.11770 -0.389 0.702
## host_time_point3 -0.23527 0.13733 -1.713 0.106
##
## (Dispersion parameter for quasipoisson family taken to be 0.05292627)
##
## Null deviance: 0.98270 on 17 degrees of freedom
## Residual deviance: 0.83318 on 16 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4Reproduction
reprohostevol <- ggplot(LA_means_hostevol,
aes(x=host_time_point, y=mn_clutches, fill=host_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#808080")) +
scale_color_manual(values=c("#5445b1", "#808080")) +
xlab("Host time point") +ylab("Mean # clutches") +
labs(color= "Host time point") + labs(fill="Host time point") +
theme_cowplot()
reprohostevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_hostevol$log_mn_clutches <- log((LA_means_hostevol$mn_clutches+1))
mod3b <- glm(log_mn_clutches ~ host_time_point,
family = "gaussian",
data = LA_means_hostevol)
plot(mod3b) 
summary(mod3b)##
## Call:
## glm(formula = log_mn_clutches ~ host_time_point, family = "gaussian",
## data = LA_means_hostevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61750 -0.25822 -0.03508 0.10674 1.22833
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6175 0.2192 2.817 0.0124 *
## host_time_point3 -0.3593 0.2485 -1.446 0.1676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1921489)
##
## Null deviance: 3.4760 on 17 degrees of freedom
## Residual deviance: 3.0744 on 16 degrees of freedom
## AIC: 25.271
##
## Number of Fisher Scoring iterations: 2Relative reproduction
relreprohostevol <- ggplot(LA_means_hostevol,
aes(x=host_time_point, y=rel_clutches_mn, fill=host_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#808080")) +
scale_color_manual(values=c("#5445b1", "#808080")) +
xlab("Host time point") +ylab("Relative # clutches") +
labs(color= "Host time point") + labs(fill="Host time point") +
theme_cowplot()
relreprohostevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_hostevol$log_rel_clutches_mn <- log((LA_means_hostevol$rel_clutches_mn+1))
mod3brel <- glm(log_rel_clutches_mn ~ host_time_point,
family = "gaussian",
data = LA_means_hostevol)
plot(mod3brel) 
summary(mod3brel)##
## Call:
## glm(formula = log_rel_clutches_mn ~ host_time_point, family = "gaussian",
## data = LA_means_hostevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.12727 -0.02668 -0.00902 0.01072 0.33810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12727 0.05063 2.513 0.0230 *
## host_time_point3 -0.10059 0.05741 -1.752 0.0989 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.01025551)
##
## Null deviance: 0.19557 on 17 degrees of freedom
## Residual deviance: 0.16409 on 16 degrees of freedom
## AIC: -27.477
##
## Number of Fisher Scoring iterations: 2Spores
sporeshostevol <- ggplot(LA_means_hostevol,
aes(x=host_time_point, y=mn_spores, fill=host_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#808080")) +
scale_color_manual(values=c("#5445b1", "#808080")) +
xlab("Host time point") +ylab("Mean # spores \nper infected host") +
labs(color= "Host time point") + labs(fill="Host time point") +
scale_y_log10() +
theme_cowplot()
sporeshostevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)LA_means_hostevol$log_mn_spores <- log(LA_means_hostevol$mn_spores)
mod3c <- glm(log_mn_spores ~ host_time_point,
family = "gaussian",
data = LA_means_hostevol)
plot(mod3c) 
summary(mod3c)##
## Call:
## glm(formula = log_mn_spores ~ host_time_point, family = "gaussian",
## data = LA_means_hostevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1748 -0.3479 0.1552 0.4171 1.0125
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.470 0.306 37.480 <2e-16 ***
## host_time_point3 0.332 0.347 0.957 0.353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.3746263)
##
## Null deviance: 6.337 on 17 degrees of freedom
## Residual deviance: 5.994 on 16 degrees of freedom
## AIC: 37.289
##
## Number of Fisher Scoring iterations: 2Parasite growth rate
LA_means_hostevol$paragrowth <- (LA_means_hostevol$mn_spores)/(LA_means_hostevol$mn_lifespan)
paragrowthhostevol <- ggplot(LA_means_hostevol,
aes(x=host_time_point, y=paragrowth, fill=host_time_point)) +
geom_violin(show.legend = FALSE) +
geom_jitter(shape=16, position=position_jitter(width=0.3, height=0), alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values=c("#5445b1", "#808080")) +
scale_color_manual(values=c("#5445b1", "#808080")) +
xlab("Host time point") +ylab("Parasite growth rate \n(spores per day)") +
labs(color= "Host time point") + labs(fill="Host time point") +
theme_cowplot()
paragrowthhostevol
# ggsave(here("figures", "lifespansametime.pdf"), lifespansametime, units = "in", width = 7, height = 5, dpi = 300)mod3d <- glm(paragrowth ~ host_time_point,
family = "gaussian",
data = LA_means_hostevol)
plot(mod3d) 
summary(mod3d)##
## Call:
## glm(formula = paragrowth ~ host_time_point, family = "gaussian",
## data = LA_means_hostevol)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3060.5 -1781.2 163.5 1234.8 4602.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3719.2 1119.4 3.322 0.00431 **
## host_time_point3 763.7 1269.3 0.602 0.55581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5012430)
##
## Null deviance: 82013591 on 17 degrees of freedom
## Residual deviance: 80198882 on 16 degrees of freedom
## AIC: 332.66
##
## Number of Fisher Scoring iterations: 2Combined figure
hostevolplot <- plot_grid(lifespanhostevol, rellifespanhostevol, reprohostevol, relreprohostevol, sporeshostevol, paragrowthhostevol, labels = "auto", ncol = 2, align = "v")
hostevolplot
ggsave(here("figures", "hostevolplot.pdf"), hostevolplot, units = "in", width = 10, height = 7, dpi = 300)
ggsave(here("figures", "hostevolplot.jpg"), hostevolplot, units = "in", width = 10, height = 7, dpi = 300)